1. Field of Invention
This invention relates to the automation of the packaging and assembly of optoelectronics. Specifically, the present invention relates to the provision of intelligent control and system level modeling in order to obtain high performance, low cost automation of assembly and packaging.
2. Description of the Related Technology
The current trend in optical microsystem design is to exploit advanced devices and new system architectures to achieve greater system performance, such as higher data rates or brighter displays. Advancements in the optics field may have driven up the demand for complicated devices, however the packaging and assembly of these complicated devices has not increased in sophistication. As a consequence the current methods of packaging and assembling optoelectronics do not produce the most favorable results.
Examples of new devices increasing optical capacity are numerous. Research is being performed in micro-electrical-mechanical systems (MEMS), in which micro-machined mirrors steer an optical signal through a switching network. Next generation systems, supporting terabit/sec communication are being designed with thin film electro-optic modulators, low-loss hetero-structure waveguides and photonic integrated circuits, and high efficiency, edge-emitting, multi-wavelength quantum dot laser arrays. Other nanostructures are being used in WDM systems for optical signal processing, polarization control of VCSEL lasers, all-optical buffers, and micro-resonators. Beyond the telecommunications field, there have been advances in devices for displays and sensors. These include, holographic polymer dispersed liquid crystals, photonic crystals, and nano-tubes.
Although there has been much advancement in the field of complex optical devices, there has been little to no advancement in the assembly or packaging of these products. However, to push towards the theoretical limits of optical microsystems, accurate alignment and packaging of multi-domain systems is required. Packaging is a challenging problem, as systems are typically manually aligned. This technique is labor intensive, slow, and can lead to a poor performance of the optical system. Even with recent progress in the development of devices and Microsystems, the packaging and assembly of these systems remains as a possible critical limiting factor to their commercial success.
Automation is the key to high volume, low cost, and high consistency manufacturing, while ensuring performance, reliability and quality. There is a growing interest in the development of automation techniques for photonic alignment and packaging, as the optical microsystem industry desires the benefits of automation experienced by, for example, the semiconductor industry. However, the photonic community cannot simply use the same automation processes as the semiconductor industry. The equipment is not optimized for optoelectronic packaging automation since the optical and geometric axes of these optical Microsystems are often not aligned with one another. This points out the fundamental difference between electrical, or semiconductor automation, and optical automation. In the electrical domain, a good attachment occurs between two components when they physically touch and solder flows between them. However, in the optical domain, not only is a good connection needed, an exact orientation alignment is required. As a result, packaging costs currently account for 60-80% of the entire photonic component cost.
The current automation technique used has many limitations. First, if the optical wavefront is not a symmetric uni-mode function, the control algorithm can get “caught” at local power maximums instead of the global maximum of the entire wavefront. This error can yield a dramatic loss in power efficiency, SNR, and BER for the assembled product. Therefore, as the complexity of the optical wavefront increases, possibly with the addition of complex devices such as MEMS and diffractive optical elements (DOE), the current technique of alignment might not yield maximum system performance.
Secondly, since multi-space searches are employed with a gradient ascent algorithm, the convergence time of the alignment equipment will depend on factors such as the control resolution and processing power. A package with multiple degrees of freedom may result in a delayed assembly line, since the gradient ascent algorithm for multiple axes is very slow and sometimes non-converging. This increases the cost of the automation process. Lastly, current servos and control (PID) loops deployed for semiconductor equipment do not employ process knowledge base data in the loop.
Most of the existing photonic automation systems couple laser diodes to fiber, fiber to fiber, or waveguide (on an integrated circuit) to a fiber. The state-of-the-art technology is based on industrial and semiconductor automation, robotics, motion control, sensor technology, and existing capital equipment. For uni-mode optical signals, such as Gaussian shaped beams emitted from laser sources, waveguides, and fibers, photonic automation is advancing. However, to date, no significant defined standard has been developed to implement automation for general optical systems. Therefore, the majority of production lines for photonic systems are still only poorly automated.
Currently, photonic alignment research is performed in academic institutions by examining how packaging and alignment can be designed in the system substrate through micromachining. In addition, some leading automation and optical component companies have realized the importance of automation for photonic systems. The control loop implemented by these industries is described in and seen in FIG. 1.
The technique in FIG. 1 is based on a combination of visual inspection and maximizing power alignments. This work has shown promise for the support of optical automation for simple uni-modal power distributions, as the Proportional Integral Derivative (PID) loops converge to a single mode. The loop 100 in FIG. 1 is called the servo-feedback loop. The servo-feedback loop performs a gradient ascent 108 on the measured optical power by comparing consecutive power readings Pk and Pk−1 112 at configurations xk and xk−1. A gradient, (Pk−Pk−1)/(xk−xk−1), is formed which guides the axis motion to the next configuration, xk+1:xk+1=xk+η((Pk−Pk−1)/(xk−xk−1))                where, η is the gradient accent coefficient, which is the resolution of the step.        
Currently, the control loop is initiated to a set point (x0) by a vision system 102. Key shapes of the fiber or waveguide are searched for in the field of vision of a CCD camera focused at the alignment and attachment point. From these searches, the automation software “visualizes” the desired link, and initializes the control motors with a determined set point via the initialization loop 104. After determining the vision set point, the alignment is fine-tuned by the local gradient ascent search to a local power maximum, as described in FIG. 1. Each axis of motion is independently controlled, and typically, the number of controlled axes is quite small. To obtain the required power measurement, a laser is used to excite the system and a power meter is attached to the output fiber, this can be seen in step 106. In the event that the system is not being aligned correctly the system stops and the alignment is fixed in step 110. In efforts to decrease the amount of time to determine the peak power mode, efficient positioning algorithms have been implemented, based on the assumption that the power distribution will always be a uni-mode (Gaussian) shape. The algorithm picks three initial points and measures the power at each. From these results, the algorithm determines three new points based on a Gaussian distribution, and continues this process until the power peak is found.
Due to the limitations of the current automation techniques discussed above, there is a need for a knowledge based modeling process for the automation of photonic systems in order to reach the potential of the high-capacity optical systems in which packaging and automation are keys to performance and cost.